Yazar "Akhan Baykan, Nurdan" seçeneğine göre listele

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  • Küçük Resim
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    Edge detection of aerial images using artificial bee colony algorithm
    (Kırgızistan Türkiye Manas Üniversitesi, 2022-06-30) Yelmenoğlu, Elif Deniz; Akhan Baykan, Nurdan
    Edge detection techniques are the one of the best popular and significant implementation areas of the image processing. Moreover, image processing is very widely used in so many fields. Therefore, lots of methods are used in the development and the developed studies provide a variety of solutions to problems of computer vision systems. In many studies, metaheuristic algorithms have been used for obtaining better results. In this paper, aerial images are used for edge information extraction by using Artificial Bee Colony (ABC) Optimization Algorithm. Procedures were performed on gray scale aerial images which are taken from RADIUS/DARPA-IU Fort Hood database. Initially bee colony size was specified according to sizes of images. Then a threshold value was set for each image, which related with images’ standard deviation of gray scale values. After the bees were distributed, fitness values and probability values were computed according to gray scale value. While appropriate pixels were specified, the other ones were being abandoned and labeled as banned pixels therefore bees never located on these pixels again. So the edges were found without the need to examine all pixels in the image. Our improved method’s results are compared with other results found in the literature according to detection error and similarity calculations’. All the experimental results show that ABC can be used for obtaining edge information from images.
  • Küçük Resim
    Yayın
    Edge detection of aerial images using artificial bee colony algorithm
    (Selcuk University Faculty of Technology, 2021-11) Yelmenoğlu, Elif Deniz; Akhan Baykan, Nurdan; Taşdemir, Şakir
    Edge detection techniques are the one of the best popular and significant implementation areas of the image processing. Moreover, image processing is very widely used in so many fields. Therefore, lots of methods are used in the development and the developed studies provide a variety of solutions to problems of computer vision systems. In many studies, metaheuristic algorithms have been used for obtaining better results. In this paper, aerial images are used for edge information extraction by using Artificial Bee Colony (ABC) Optimization Algorithm. Procedures were performed on gray scale aerial images which are taken from RADIUS/DARPA-IU Fort Hood database. Initially bee colony size was specified according to sizes of images. Then a threshold value was set for each image, which related with images’ standard deviation of gray scale values. After the bees were distributed, fitness values and probability values were computed according to gray scale value. While appropriate pixels were specified, the other ones were being abandoned and labeled as banned pixels therefore bees never located on these pixels again. So the edges were found without the need to examine all pixels in the image. Our improved method’s results are compared with other results found in the literature according to detection error and similarity calculations’. All the experimental results show that ABC can be used for obtaining edge information from images.
  • Küçük Resim
    Yayın
    Edge detection using artificial bee colony algorithm (ABC)
    (IACSIT, 2013-11-21) Yiğitbaşı, Elif Deniz; Akhan Baykan, Nurdan
    Edge detection methods in the field of image processing are an important application area. Currently, image processing is being exploited in many areas. For this reason, methods used in developing more and more every day and studies which is about computer vision systems are being developed for less errors. Optimization algorithms have been used for better results in so many studies. In this paper, Artificial Bee Colony (ABC) Optimization Algorithm is used for edge detection which is about gray scale images. First, ABC algorithm is explained. Following, edge detection and edge detection with ABC algorithm are clarified. Finally, results are showed. Results show that the proposed method can be applied for edge detection operations.